Search Results for author:
Found 56 papers, 10 papers with code
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing
Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states.
VTLayout: Fusion of Visual and Text Features for Document Layout Analysis
In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems
In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages.
Episode Adaptive Embedding Networks for Few-shot Learning
Few-shot learning aims to learn a classifier using a few labelled instances for each class.
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance.
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
The proposed model is shown to generalize well to both homophilic and heterophilic graphs.
The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud
The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably.
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
Deep Anti-aliasing of Whole Focal Stack Using its Slice Spectrum
The paper aims at removing the aliasing effects for the whole focal stack generated from a sparse 3D light field, while keeping the consistency across all the focal layers. We first explore the structural characteristics embedded in the focal stack slice and its corresponding frequency-domain representation, i. e., the focal stack spectrum (FSS).
Chiral effective Lagrangian for excited heavy-light mesons from QCD
We derive the chiral effective Lagrangian for excited heavy-light mesons from QCD under proper approximations.
High Energy Physics - Phenomenology
Global Node Attentions via Adaptive Spectral Filters
The proposed model has been shown to generalize well to both assortative and disassortative graphs.
ErGAN: Generative Adversarial Networks for Entity Resolution
Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets.
Chiral effective Lagrangian for heavy-light mesons from QCD: $1/m_{Q}$ correction
The low energy constants in the effective Lagrangian are expressed in terms of the light quark self-energy and heavy quark mass $m_Q$.
High Energy Physics - Phenomenology
Emotion Classification by Jointly Learning to Lexiconize and Classify
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR.
Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain
One of the strengths of traditional convolutional neural networks (CNNs) is their inherent translational invariance.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
PEL-BERT: A Joint Model for Protocol Entity Linking
Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently.
Divergence-Augmented Policy Optimization
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data.
Knowledge Tracing with Sequential Key-Value Memory Networks
Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence.
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets).
Ranked #1 on
Node Classification
on NELL
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Learning to Sample: an Active Learning Framework
However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning.
Arena: a toolkit for Multi-Agent Reinforcement Learning
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.
Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition
Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples.
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
We prove that the light field is a 2D series, thus, a specifically designed CNN-LSTM network is proposed to capture the continuity property of the EPI.
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.
Exponentially Weighted Imitation Learning for Batched Historical Data
We consider deep policy learning with only batched historical trajectories.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras
Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model.
Dense Light Field Reconstruction From Sparse Sampling Using Residual Network
A light field records numerous light rays from a real-world scene.
End-to-end driving simulation via angle branched network
Imitation learning for end-to-end autonomous driving has drawn attention from academic communities.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Light Field Segmentation From Super-pixel Graph Representation
The large volume of input data and the redundancy of light field make it an open challenge.
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities.
Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms
To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i. e., arms).
4D Light Field Superpixel and Segmentation
Superpixel segmentation of 2D image has been widely used in many computer vision tasks.
Collective Vertex Classification Using Recursive Neural Network
In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
Discovering and Deciphering Relationships Across Disparate Data Modalities
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.
Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration
The geometry of the recovered scene structure is affected by the calibration of the plenoptic camera significantly.
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
Occlusion is one of the most challenging problems in depth estimation.
DARI: Distance metric And Representation Integration for Person Verification
The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately.
Ranked #8 on
Person Re-Identification
on SYSU-30k
(using extra training data)
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics (i. e. the assigned labels of the target and the references).
Aliasing Detection and Reduction in Plenoptic Imaging
When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts.